Zhenhui Jessie Li

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 16 papers; trusts 212 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 157 citations into it; #11,468 of 35,345 by trust here, #10,566 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #6,626 of 21,575 by trust here, #7,092 by citations
• Representation & Self-Supervised Learning — 52 citations into it
• Information Systems — 45 citations into it
• Control and Systems Engineering — 43 citations into it; #2,131 of 8,257 by trust here, #2,078 by citations
• Building and Construction — 42 citations into it; #389 of 2,909 by trust here, #378 by citations
• Transportation — 36 citations into it
• Reinforcement Learning — 31 citations into it

Whom Zhenhui Jessie Li trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Marco A. Wiering
7. Yann LeCun
8. Chelsea Finn
9. Kaiming He
10. Ilya Sutskever

Who trusts Zhenhui Jessie Li overall

1. Yu Zheng
2. Xianchao Zhang
3. Xiaotong Zhang
4. Gowtham Atluri
5. Hua Wei
6. Yu Zhang
7. Ye Yuan
8. Charu C. Aggarwal
9. 刘晗
10. Chacha Chen

Papers from Zhenhui Jessie Li's perspective

Ordered by how strongly the researchers Zhenhui Jessie Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Online Structured Meta-learning (2020) — 29 citations
• Hierarchically Structured Meta-learning (2019) — 223 citations
• Generalized Fisher Score for Feature Selection (2011) — 848 citations
• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018) — 748 citations
• CoLight: Learning Network-level Cooperation for Traffic Signal Control (2019) — 527 citations
• PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (2019) — 485 citations
• A Survey on Traffic Signal Control Methods (2019) — 297 citations
• CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (2019) — 372 citations
• Graph Few-shot Learning via Knowledge Transfer (2019) — 194 citations
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018) — 1,258 citations
• IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (2018) — 771 citations
• Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2018) — 833 citations
• Differentially private data cubes: optimizing noise sources and consistency (2011) — 163 citations
• Learning a Kernel for Multi-Task Clustering (2011) — 60 citations
• Mining periodic behaviors for moving objects (2010) — 322 citations
• Swarm (2010) — 366 citations